Historically, time series data is considered as being continuous over time. The underlying assumption is that a smooth, continuous series exists, that this series is being sampled at regular intervals, and that the sampling error is uniform due to the sampling intervals being evenly spaced. These assumptions fit well with data such as the U.S. Gross National Product, national unemployment data, national housing starts, and other smooth aggregate series. If such data is aggregated to a quarterly series, then a smooth seasonal and trend model is apparent.
With the advent of modern computing systems, vast quantities of data are collected in real time. This data is often detailed data such as the sales volume of individual items identified by their unique UPC code. Much of this retail data is seasonal. Consumers buy sweaters in the fall and shorts in the spring. Even when data is aggregated, it can display a seasonal pattern characterized by large periods of inactivity. When an evenly spaced interval is chosen that shows detail in the active period, large numbers of zeros appear in the inactive period.